Instructions to use TechxGenus/deepseek-coder-1.3b-instruct-AQLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TechxGenus/deepseek-coder-1.3b-instruct-AQLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TechxGenus/deepseek-coder-1.3b-instruct-AQLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TechxGenus/deepseek-coder-1.3b-instruct-AQLM")
model = AutoModelForCausalLM.from_pretrained("TechxGenus/deepseek-coder-1.3b-instruct-AQLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TechxGenus/deepseek-coder-1.3b-instruct-AQLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TechxGenus/deepseek-coder-1.3b-instruct-AQLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/deepseek-coder-1.3b-instruct-AQLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TechxGenus/deepseek-coder-1.3b-instruct-AQLM

SGLang

How to use TechxGenus/deepseek-coder-1.3b-instruct-AQLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TechxGenus/deepseek-coder-1.3b-instruct-AQLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/deepseek-coder-1.3b-instruct-AQLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TechxGenus/deepseek-coder-1.3b-instruct-AQLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TechxGenus/deepseek-coder-1.3b-instruct-AQLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TechxGenus/deepseek-coder-1.3b-instruct-AQLM with Docker Model Runner:
```
docker model run hf.co/TechxGenus/deepseek-coder-1.3b-instruct-AQLM
```

deepseek-coder-1.3b-instruct-AQLM / config.json

TechxGenus

Upload folder using huggingface_hub

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	{
	"vocab_size": 32256,
	"max_position_embeddings": 16384,
	"hidden_size": 2048,
	"intermediate_size": 5504,
	"num_hidden_layers": 24,
	"num_attention_heads": 16,
	"num_key_value_heads": 16,
	"hidden_act": "silu",
	"initializer_range": 0.02,
	"rms_norm_eps": 1e-06,
	"pretraining_tp": 1,
	"use_cache": true,
	"rope_theta": 100000,
	"rope_scaling": {
	"factor": 4.0,
	"type": "linear"
	},
	"attention_bias": false,
	"attention_dropout": 0.0,
	"torch_dtype": "float16",
	"tie_word_embeddings": false,
	"architectures": [
	"LlamaForCausalLM"
	],
	"bos_token_id": 32013,
	"eos_token_id": 32021,
	"_name_or_path": "deepseek-ai/deepseek-coder-1.3b-instruct",
	"transformers_version": "4.38.2",
	"model_type": "llama",
	"quantization_config": {
	"quant_method": "aqlm",
	"nbits_per_codebook": 16,
	"num_codebooks": 1,
	"out_group_size": 1,
	"in_group_size": 8,
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	}